Enhanced sampling without borders: on global biasing functions and how to reweight them

Area of Science:

• Computational Biology
• Biophysics
• Biochemistry

Background:

• Molecular dynamics (MD) simulations offer atomistic insights into biomolecular motions but are limited by computational cost.
• Observing biologically relevant timescales requires overcoming the limitations of conventional MD simulations.

Purpose of the Study:

• To provide a comprehensive overview of enhanced sampling techniques in MD simulations.
• To discuss established methods and novel advances in global biasing functions.
• To analyze the benefits and limitations of reweighting schemes for retrieving unbiased data.

Main Methods:

• Focus on enhanced sampling techniques employing global biasing functions.
• Review established and emerging enhanced sampling algorithms.
• Examine common reweighting schemes for analyzing biased simulation ensembles.

Main Results:

• Established and new global enhanced sampling methods are presented.
• Analysis of benefits and limitations of various reweighting strategies is provided.
• Critical assumptions and implications of these methods are discussed.

Conclusions:

• Enhanced sampling techniques, particularly global biasing, are crucial for extending accessible timescales in MD simulations.
• Careful application and understanding of reweighting schemes are necessary to obtain unbiased results.
• Global enhanced sampling offers broad applicability but requires awareness of its inherent uncertainties.